Competing requirements vehicle manufacturers have to meet have never been more complex. They have to create an exciting product, one that sounds good, feels good, and delivers an exciting experience through more and more intelligent systems that are integrated with the consumer’s world. At the same time, the product has to become more fuel efficient, lighter, and more sustainable every year. Engineers have an increasingly difficult job finding exactly the right balance between many conflicting needs.

All the intelligence in vehicles has resulted in, right now, over 20 million lines of code in the average vehicle and an ever growing set of requirements that must be managed and validated against. At the same time, several leading automakers, like Volkswagen with the MQB [Modularer Querbaukasten, which means Modular Transverse Matrix] or Toyota with TNGA [Toyota New Global Architecture] are driving toward modularity, building global and cross-brand systems of compatible components. While componentization helps reduce the complexity and degree variation, it adds to the engineering challenge of understanding the behavior, durability, and overall customer experience with a component that is re-used in many vehicle configurations.

Frontloading decision making

More than ever, decisions an engineer makes solving a certain problem in a certain context can influence how the vehicle behaves or is perceived, in a way the engineer can’t easily predict. This is the core of the challenge for PLM platforms: what is needed is a way to bring the knowledge required to make critical decisions to each person involved in the development and manufacturing of the vehicle, exactly when they need it and in the exact context in which they need it.

The system needs to enable engineers to make the correct decision earlier in the process, when the impact of changes is much less expensive than if they occur later. The system needs to involve the right experts, who can help impact quality, durability, performance, and manufacturing cost, much earlier.

In essence, the systems needs to “front-load” decision making by moving the impact of these downstream functions earlier, closing the loop between decisions and verification, ensuring that decisions made early are the right ones.

Next generation PLM

The next generation of PLM provides an immersive environment for lifecycle decision-making. To do that, we believe people need to become more intrinsically aware. Information should surround them and be brought to them, but not all of it, only what’s relevant and in the right context. This will help them make integrated decisions in the context of systems, and do so on time and with greater accuracy than ever before.

The goal is to build an immersive decision-making environment where every person in the product lifecycle gets exactly the knowledge they need, exactly when they need it, and exactly in the context of the decision they have to make. Such an environment turns complexity into useful knowledge that gives automakers a competitive edge.

It does this by:

1. Providing one transparent environment for all users across the enterprise, from marketing, quality, design, and engineering, to manufacturing.

2. Gathering data from the many data sources that are key to make the right decisions.

3. Providing tools to define the architecture and models describing the physics, controls, and behavior of a vehicle.

4. Ensuring that only the right information is delivered, and delivered in the context of the user’s job.

It is possible to engineer the customer experience using systems-driven product development. The method of systems engineering has been embraced for decades by the aerospace and defense industry and increasingly by the automotive industry. But systems engineering is itself sometimes referred to as a complex process. Not all engineers across the company can understand all the complexities of how systems engineering works. In fact, systems engineering is often done outside of the core design activities, by the few experts within the company often taking it off the critical path.

Companies can gain great leverage from the knowledge produced in systems engineering, so they must find a way to use the models created by systems engineers to be able to do their work in the context of all the complex systems that surround them. So how can organizations leverage systems engineering without being overwhelmed by the complexity of systems engineering itself?

That is PLM’s job. PLM ensures the complexity of the product is well defined and understood by capturing it in all within PLM and then having PLM make sure it’s consumable by all the engineers, and others, throughout the product lifecycle.

4 elements of systems driven development

Four key elements of systems driven product development are:

1. Openness. No one software product can do the job. When automakers and top suppliers inventory the number of development, simulation, engineering, and manufacturing software tools they apply, they find hundreds. That means the PLM system’s openness, the ability to leverage the data that others need without getting clogged with transient information, is a key success factor.

2. Single configuration management. PLM must provide an architecture that determines variation and defines the myriad of theoretical and practical configurations.

3. Change and schedule management. PLM must provide the enterprise change and schedule management process that ensures alignment across the multitude of applications where change happens.

4. Architecting and simulating the customer experience. Program engineers need to understand fuel consumption, driving behavior, noise and vibration, and cost of a vehicle in many configurations. To do that, they need accurate models of the different physical characteristics as well as the controls software that they can “assemble” into a simulation of the vehicle and “test drive” on the computer. This requires the PLM system to:

Provide the architecture that defines the dependencies and models describing the interactions of subsystems.

Provide the correct data, the right version of all mechanical, software, and electronics parameters, even if they come from databases outside of PLM.

Be able to synthesize models from several specialized tools and applications.

Together, these foundational elements can enable everyone in the enterprise who can affect a product to be better to do so.

Leveraging the tools

How can the enterprise be enabled to leverage systems engineering? Imagine being able to use a “whole vehicle model” to check if a new requirement for driving comfort is realistic for all the different models (maybe from different brands) for which it is intended. In today’s practice, these targets are set based on market assessment and an educated guess on what is achievable. What if you could run a fairly accurate simulation to check that?

Application engineers have the constant challenge to find the best fit of technologies to market needs, cost targets, and performance goals. Their daily work becomes radically more efficient and accurate if they have a reliable way to see how new or modified technology will affect the performance targets of a vehicle program.

To illustrate how far the social product lifecycle management (SPLM) solution is down this road, following are a few examples of current customer implementations that serve as milestones toward realizing this vision.

Case study: GM, from math to lab

The embedded software space is where the benefit of frontloading the process, of moving from the road to the lab, is most obvious. One of the best examples is the process of engine calibration. Setting the parameters in the engine controls software ultimately determines fuel efficiency, performance, and sound. In the traditional process, a major part of this engineering activity happens in an actual vehicle. The algorithms themselves, however, are developed early on.

Integration of the algorithms (controls models) and reliable and proven vehicle level models allows virtual calibration, which reduces effort needed for the actual, prototype-based calibration to almost half of what it used to be, more importantly, shortening the time needed.

Case study: Daimler, optimization across lines

Daimler Benz is among key customers of LMS (a Siemens business) in the area of driving dynamics. As every other automaker, Daimler struggles with the growing complexity of the product line and has been seeking a way to be earlier with optimizing driving dynamics. In the solution has been the integration of the model-based, multi-body simulation and building the feedback loop between that simulation and actual physical test. That integration makes the models accurate enough for engineers to rely on simulation to optimize ride and handling and be confident that the actual vehicle will behave the way they want.